@article{andersonSystemsModelSDG2022,
  title = {A Systems Model of {{SDG}} Target Influence on the 2030 {{Agenda}} for {{Sustainable Development}}},
  author = {Anderson, Carl C. and Denich, Manfred and Warchold, Anne and Kropp, Jürgen P. and Pradhan, Prajal},
  date = {2022-07},
  journaltitle = {Sustainability Science},
  shortjournal = {Sustain Sci},
  volume = {17},
  number = {4},
  pages = {1459--1472},
  issn = {1862-4065, 1862-4057},
  doi = {10.1007/s11625-021-01040-8},
  url = {https://link.springer.com/10.1007/s11625-021-01040-8},
  urldate = {2023-02-17},
  abstract = {Abstract                            The Sustainable Development Goals (SDGs) were adopted by the United Nations in 2015 as part of the “2030 Agenda for Sustainable Development” and aim to address issues ranging from poverty and economic growth to climate change. Efforts to tackle one issue can support or hinder progress towards others, often with complex systemic interactions. Thus, each of the SDGs and their corresponding targets may contribute as levers or hurdles towards achieving other SDGs and targets. Based on SDG indicator data, we create a systems model considering influence among the SDGs and their targets. Once assessed within a system, we find that more SDGs and their corresponding targets act as levers towards achieving other goals and targets rather than as hurdles. In particular, efforts towards SDGs 5 (               Gender Equality               ) and 17 (               Partnerships for the Goals               ) may accelerate progress, while SDGs 10 (               Reduced Inequalities               ) and 16 (               Peace, Justice and Strong Institutions               ) are shown to create potential hurdles. The model results can be used to help promote supportive interactions and overcome hindering ones in the long term.},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/86KLNM2T/Anderson 等 - 2022 - A systems model of SDG target influence on the 203.pdf;/home/scenerymc/Zotero/storage/U37MC6L9/Anderson 等 - 2022 - A systems model of SDG target influence on the 203.pdf}
}

@article{bellantuonoSustainableDevelopmentGoals2022,
  title = {Sustainable Development Goals: Conceptualization, Communication and Achievement Synergies in a Complex Network Framework},
  shorttitle = {Sustainable Development Goals},
  author = {Bellantuono, Loredana and Monaco, Alfonso and Amoroso, Nicola and Aquaro, Vincenzo and Lombardi, Angela and Tangaro, Sabina and Bellotti, Roberto},
  date = {2022-12},
  journaltitle = {Applied Network Science},
  shortjournal = {Appl Netw Sci},
  volume = {7},
  number = {1},
  pages = {14},
  issn = {2364-8228},
  doi = {10.1007/s41109-022-00455-1},
  url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00455-1},
  urldate = {2023-02-17},
  abstract = {Abstract             In this work we use a network-based approach to investigate the complex system of interactions among the 17 Sustainable Development Goals (SDGs), that constitute the structure of the United Nations 2030 Agenda for a sustainable future. We construct a three-layer multiplex, in which SDGs represent nodes, and their connections in each layer are determined by similarity definitions based on conceptualization, communication, and achievement, respectively. In each layer of the multiplex, we investigate the presence of nodes with high centrality, corresponding to strategic SDGs. We then compare the networks to establish whether and to which extent similar patterns emerge. Interestingly, we observe a significant relation between the SDG similarity patterns determined by their achievement and their communication and perception, revealed by social network data. The proposed framework represents an instrument to unveil new and nontrivial aspects of sustainability, laying the foundation of a decision support system to define and implement SDG achievement strategies.},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/GBX6XYW5/Bellantuono 等 - 2022 - Sustainable development goals conceptualization, .pdf;/home/scenerymc/Zotero/storage/JH7X4EZC/Bellantuono 等 - 2022 - Sustainable development goals conceptualization, .pdf}
}

@article{breuerTranslatingSustainableDevelopment2019,
  title = {Translating {{Sustainable Development Goal}} ({{SDG}}) {{Interdependencies}} into {{Policy Advice}}},
  author = {Breuer, Anita and Janetschek, Hannah and Malerba, Daniele},
  date = {2019-04-08},
  journaltitle = {Sustainability},
  shortjournal = {Sustainability},
  volume = {11},
  number = {7},
  pages = {2092},
  issn = {2071-1050},
  doi = {10.3390/su11072092},
  url = {https://www.mdpi.com/2071-1050/11/7/2092},
  urldate = {2023-02-20},
  abstract = {The 17 Sustainable Development Goals (SDGs) of the 2030 Agenda, and their 169 targets, are interdependent and interlinked. The successful implementation of all SDGs will rely upon disentangling complex interactions between the goals and their targets. This implies that implementing the SDGs requires cross-sectoral processes to foster policy coherence. Over recent years, academic research has produced a number of different proposals for categorizing the SDGs, systematically mapping the linkages between them, and identifying the nature of their interdependencies. The aim of this review article is to provide ideas of how to move from generic appraisals of SDG interdependencies towards translating these interdependencies into policy action. To do so, the article first provides an overview of existing frameworks for the systematic conceptualization of the SDGs and the interlinkages and interdependencies between them. Secondly, the article critically discusses advantages and limitations of these frameworks, with a particular focus on methodological weaknesses, practical applicability to specific contexts, and utility for the development of policy strategies for coherent SDG planning and implementation. Based on this discussion, the article proposes a roadmap for how research on interdependencies can meaningfully provide orientation for policy action.},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/36IHWMGT/Breuer 等 - 2019 - Translating Sustainable Development Goal (SDG) Int.pdf}
}

@article{dalampiraMappingSustainableDevelopment2020,
  title = {Mapping {{Sustainable Development Goals}}: {{A}} Network Analysis Framework},
  shorttitle = {Mapping {{Sustainable Development Goals}}},
  author = {Dalampira, Evropi‐Sofia and Nastis, Stefanos A.},
  date = {2020-01},
  journaltitle = {Sustainable Development},
  shortjournal = {Sustainable Development},
  volume = {28},
  number = {1},
  pages = {46--55},
  issn = {0968-0802, 1099-1719},
  doi = {10.1002/sd.1964},
  url = {https://onlinelibrary.wiley.com/doi/10.1002/sd.1964},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/BM77PUBM/Dalampira 和 Nastis - 2020 - Mapping Sustainable Development Goals A network a.pdf}
}

@article{dongjinwei;chenyu;zhouyan;yinjiadi;zhaorui;DiqiudashujuzhichengkechixufazhanmubiaoxietongyuquanhengyanjiuJinzhanyuzhanwang2021,
  title = {地球大数据支撑可持续发展目标协同与权衡研究：进展与展望},
  author = {董金玮;陈玉;周岩;殷嘉迪;赵芮;},
  date = {2021},
  journaltitle = {中国科学院院刊},
  pages = {950--962},
  issn = {1000-3045},
  doi = {10.16418/j.issn.1000-3045.20210714003},
  abstract = {联合国《改变我们的世界:2030年可持续发展议程》是各国实现经济、社会和环境共同发展的重要指南。当前,该议程的17个可持续发展目标(SDGs)的监测和评价已取得重要进展,但各SDGs间相互作用,特别是SDGs间的协同和权衡关系的认知仍较有限。文章首先从全部目标关系的综合分析、典型多目标关系分析、单目标内子指标间的关系3个方面描述了当前SDGs协同与权衡的研究进展和主要发现;并针对研究中的数据瓶颈问题,剖析了地球大数据支撑多目标协同和权衡的思路及典型案例;在此基础上,对地球大数据促进SDGs协同和权衡研究进行了展望。研究表明,地球大数据在提升SDG指标数据一致性、透明性、时效性和准确性等方面能够发挥重要作用,可以改进前期基于专家知识或统计数据等方法的不足,为提升多目标协同和权衡研究的定量水平提供重要数据支撑。最后,应对SDGs权衡的挑战,提出了完善地球大数据支撑SDGs协同与权衡的方法体系并构建模拟与预警平台、加强不同领域和主体的合作、强化技术创新推动等建议。},
  issue = {08 vo 36},
  langid = {chinese},
  keywords = {Big Earth Data,sustainable development goals(SDGs),synergy,trade-off,协同,可持续发展目标,地球大数据,权衡},
  file = {/home/scenerymc/Zotero/storage/4YBFJQKT/董金玮\;陈玉\;周岩\;殷嘉迪\;赵芮\; - 2021 - 地球大数据支撑可持续发展目标协同与权衡研究：进展与展望.pdf}
}

@article{eguigurenConnectingOpenData,
  title = {Connecting {{Open Data}} and {{Sustainable Development Goals}} Using a {{Semantic Knowledge Graph}} Approach},
  author = {Eguiguren, José Eduardo and Piedra, Nelson},
  abstract = {This paper aims to present an initiative to establish links and relationships between Open Data (OD) and the Sustainable Development Goals (SDG). OD published by various organizations using the CKAN platform is highly dispersed and heterogeneous, making it harder to process and leverage those vast amounts of information in their current state. This paper approaches this opportunity by defining the steps that will allow the construction of a semantic representation of data sets found in public access data portals and each SDG using well-established ontologies and vocabularies. Doing so will provide the necessary tools to link the many resources containing data useful to the SDGs and relate them accordingly. Its implementation is a work in progress.},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/49X3WP5C/Eguiguren 和 Piedra - Connecting Open Data and Sustainable Development G.pdf}
}

@article{eppingaRankingSustainableDevelopment2022,
  title = {Ranking the Sustainable Development Goals: Perceived Sustainability Priorities in Small Island States},
  shorttitle = {Ranking the Sustainable Development Goals},
  author = {Eppinga, Maarten B. and Mijts, Eric N. and Santos, Maria J.},
  date = {2022-07},
  journaltitle = {Sustainability Science},
  shortjournal = {Sustain Sci},
  volume = {17},
  number = {4},
  pages = {1537--1556},
  issn = {1862-4065, 1862-4057},
  doi = {10.1007/s11625-022-01100-7},
  url = {https://link.springer.com/10.1007/s11625-022-01100-7},
  urldate = {2023-02-17},
  abstract = {Abstract             The Sustainable Development Goals (SDGs) aim to elicit global mobilization to implement the 2030 Agenda for Sustainable Development, and are increasingly used in support of Education for Sustainable Development (ESD). Previous studies have highlighted interdependencies between SDGs, with potential interactions between four Sustainability Domains: Economy, Governance, Planet and Society. This study aimed to assess whether people’s perception of the relative importance of the SDGs reflects recognition of the need to prioritize efforts across Domains, or whether this perception is based on different valuations of the Sustainability Domains themselves. We designed an interactive online tool in which participants used the Q-sort technique to rank the SDGs according to their subjective valuation of importance. We analyzed the rankings of 108 participants, all learners at universities in three Small Island States (SIS): Aruba, Suriname and Trinidad and Tobago. Analysis of the correlation structure among participants’ Q-sorts showed that higher perceived importance of the Society- and Economy-related SDGs 2, 3, 4, 8 and 9 traded off with lower perceived importance of the Planet-related SDGs 13, 14 and 15. Furthermore, SDG rankings of learners from Aruba occurred furthest toward the Planet-based part of this trade-off axis. For ESD programs, our method provides a novel tool to identify key interactions between SDGs that may not yet be recognized by program participants. In this way, communicating the need for simultaneous action and policy development across Sustainability Domains could be specifically tailored to the local context. Such connections may increase the effectiveness of ESD in addressing the substantial sustainability challenges facing SIS.},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/RZCFAQZE/Eppinga 等 - 2022 - Ranking the sustainable development goals perceiv.pdf}
}

@article{fotopoulouSustainGraphKnowledgeGraph2022,
  title = {{{SustainGraph}}: {{A}} Knowledge Graph for Tracking the Progress and the Interlinking among the Sustainable Development Goals’ Targets},
  shorttitle = {{{SustainGraph}}},
  author = {Fotopoulou, Eleni and Mandilara, Ioanna and Zafeiropoulos, Anastasios and Laspidou, Chrysi and Adamos, Giannis and Koundouri, Phoebe and Papavassiliou, Symeon},
  date = {2022-10-26},
  journaltitle = {Frontiers in Environmental Science},
  shortjournal = {Front. Environ. Sci.},
  volume = {10},
  pages = {1003599},
  issn = {2296-665X},
  doi = {10.3389/fenvs.2022.1003599},
  url = {https://www.frontiersin.org/articles/10.3389/fenvs.2022.1003599/full},
  urldate = {2023-02-17},
  abstract = {The development of solutions to manage or mitigate climate change impacts is very challenging, given the complexity and dynamicity of the socio-environmental and socio-ecological systems that have to be modeled and analyzed, and the need to include qualitative variables that are not easily quantifiable. The existence of qualitative, interoperable and well-interlinked data is considered a requirement rather than a desire in order to support this objective, since scientists from different disciplines will have no option but to collaborate and co-design solutions, overcoming barriers related to the semantic misalignment of the plethora of available data, the existence of multiple data silos that cannot be easily and jointly processed, and the lack of data quality in many of the produced datasets. In the current work, we present the SustainGraph, as a Knowledge Graph that is developed to track information related to the progress towards the achievement of targets defined in the United Nations Sustainable Development Goals (SDGs) at national and regional levels. The SustainGraph aims to act as a unified source of knowledge around information related to the SDGs, by taking advantage of the power provided by the development of graph databases and the exploitation of Machine Learning (ML) techniques for data population, knowledge production and analysis. The main concepts represented in the SustainGraph are detailed, while indicative usage scenarios are provided. A set of opportunities to take advantage of the SustainGraph and open research areas are identified and presented.},
  file = {/home/scenerymc/Zotero/storage/FPHKFHRQ/Fotopoulou 等 - 2022 - SustainGraph A knowledge graph for tracking the p.pdf;/home/scenerymc/Zotero/storage/LH8EYVZ3/Fotopoulou 等 - 2022 - SustainGraph A knowledge graph for tracking the p.pdf}
}

@online{Goals,
  title = {Goals},
  url = {https://www.sdgsinorder.org/goals},
  urldate = {2023-02-20},
  abstract = {The right sequence for the SDGs},
  langid = {american},
  organization = {{SDGs In Order}},
  howpublished = {\url{https://www.sdgsinorder.org/goals}}
}

@incollection{joshiKnowledgeOrganizationSystem2021,
  title = {A {{Knowledge Organization System}} for the {{United Nations Sustainable Development Goals}}},
  booktitle = {The {{Semantic Web}}},
  author = {Joshi, Amit and Morales, Luis Gonzalez and Klarman, Szymon and Stellato, Armando and Helton, Aaron and Lovell, Sean and Haczek, Artur},
  editor = {Verborgh, Ruben and Hose, Katja and Paulheim, Heiko and Champin, Pierre-Antoine and Maleshkova, Maria and Corcho, Oscar and Ristoski, Petar and Alam, Mehwish},
  date = {2021},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {12731},
  pages = {548--564},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-030-77385-4_33},
  url = {https://link.springer.com/10.1007/978-3-030-77385-4_33},
  urldate = {2023-02-17},
  isbn = {978-3-030-77384-7 978-3-030-77385-4},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/TLPW7LB6/Joshi 等 - 2021 - A Knowledge Organization System for the United Nat.pdf}
}

@article{leblancIntegrationLastSustainable2015,
  title = {Towards {{Integration}} at {{Last}}? {{The Sustainable Development Goals}} as a {{Network}} of {{Targets}}: {{The}} Sustainable Development Goals as a Network of Targets},
  shorttitle = {Towards {{Integration}} at {{Last}}?},
  author = {Le Blanc, David},
  date = {2015-05},
  journaltitle = {Sustainable Development},
  shortjournal = {Sust. Dev.},
  volume = {23},
  number = {3},
  pages = {176--187},
  issn = {09680802},
  doi = {10.1002/sd.1582},
  url = {https://onlinelibrary.wiley.com/doi/10.1002/sd.1582},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/3PV74HM2/Le Blanc - 2015 - Towards Integration at Last The Sustainable Devel.pdf}
}

@article{nguyenAnalyticHierarchyProcess,
  title = {The {{Analytic Hierarchy Process}}: {{A Mathematical Model}} for {{Decision Making Problems}}},
  author = {Nguyen, Giang Huong},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/FC4KHDTY/Nguyen - The Analytic Hierarchy Process A Mathematical Mod.pdf}
}

@article{osmanSpatialAnalysisSynergies2022,
  title = {Spatial Analysis of Synergies and Trade-Offs between the {{Sustainable Development Goals}} ({{SDGs}}) in {{Africa}}},
  author = {Osman, Adams and Mensah, Emmanuel Abeashi and Mensah, Collins Adjei and Asamoah, Yaw and Dauda, Suleman and Adu-Boahen, Kofi and Adongo, Charles Atanga},
  date = {2022-09},
  journaltitle = {Geography and Sustainability},
  shortjournal = {Geography and Sustainability},
  volume = {3},
  number = {3},
  pages = {220--231},
  issn = {26666839},
  doi = {10.1016/j.geosus.2022.07.003},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S2666683922000529},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/9ZGPUSA3/Osman 等 - 2022 - Spatial analysis of synergies and trade-offs betwe.pdf}
}

@article{pradhanSystematicStudySustainable2017,
  title = {A {{Systematic Study}} of {{Sustainable Development Goal}} ({{SDG}}) {{Interactions}}: {{A SYSTEMATIC STUDY OF SDG INTERACTIONS}}},
  shorttitle = {A {{Systematic Study}} of {{Sustainable Development Goal}} ({{SDG}}) {{Interactions}}},
  author = {Pradhan, Prajal and Costa, Luís and Rybski, Diego and Lucht, Wolfgang and Kropp, Jürgen P.},
  date = {2017-11},
  journaltitle = {Earth's Future},
  shortjournal = {Earth's Future},
  volume = {5},
  number = {11},
  pages = {1169--1179},
  issn = {23284277},
  doi = {10.1002/2017EF000632},
  url = {http://doi.wiley.com/10.1002/2017EF000632},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/PQHKXDHQ/Pradhan 等 - 2017 - A Systematic Study of Sustainable Development Goal.pdf;/home/scenerymc/Zotero/storage/X5QBIFAA/Pradhan 等 - 2017 - A Systematic Study of Sustainable Development Goal.pdf}
}

@article{pradhanSystematicStudySustainable2017a,
  title = {A {{Systematic Study}} of {{Sustainable Development Goal}} ({{SDG}}) {{Interactions}}: {{A SYSTEMATIC STUDY OF SDG INTERACTIONS}}},
  shorttitle = {A {{Systematic Study}} of {{Sustainable Development Goal}} ({{SDG}}) {{Interactions}}},
  author = {Pradhan, Prajal and Costa, Luís and Rybski, Diego and Lucht, Wolfgang and Kropp, Jürgen P.},
  date = {2017-11},
  journaltitle = {Earth's Future},
  shortjournal = {Earth's Future},
  volume = {5},
  number = {11},
  pages = {1169--1179},
  issn = {23284277},
  doi = {10.1002/2017EF000632},
  url = {http://doi.wiley.com/10.1002/2017EF000632},
  urldate = {2023-02-20},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/HKYX4JRD/Pradhan 等 - 2017 - A Systematic Study of Sustainable Development Goal.pdf}
}

@article{singhRapidAssessmentCobenefits2018,
  title = {A Rapid Assessment of Co-Benefits and Trade-Offs among {{Sustainable Development Goals}}},
  author = {Singh, Gerald G. and Cisneros-Montemayor, Andrés M. and Swartz, Wilf and Cheung, William and Guy, J. Adam and Kenny, Tiff-Annie and McOwen, Chris J. and Asch, Rebecca and Geffert, Jan Laurens and Wabnitz, Colette C.C. and Sumaila, Rashid and Hanich, Quentin and Ota, Yoshitaka},
  date = {2018-07},
  journaltitle = {Marine Policy},
  shortjournal = {Marine Policy},
  volume = {93},
  pages = {223--231},
  issn = {0308597X},
  doi = {10.1016/j.marpol.2017.05.030},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0308597X17302026},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/623BMTT4/Singh 等 - 2018 - A rapid assessment of co-benefits and trade-offs a.pdf;/home/scenerymc/Zotero/storage/YK9BDHS6/Singh 等 - 2018 - A rapid assessment of co-benefits and trade-offs a.pdf}
}

@online{SustainableDevelopmentReport,
  title = {Sustainable {{Development Report}} 2022},
  url = {https://dashboards.sdgindex.org/},
  urldate = {2023-02-20},
  abstract = {The Sustainable Development Report 2022 tracks the performance of all 193 UN Member States on the 17 Sustainable Development Goals.},
  langid = {english},
  howpublished = {\url{http://www.tp-ontrol.hu/index.php/TP_Toolbox}}
}

@online{UNSDSDGsAPI,
  title = {{{UNSD SDGs API}}},
  url = {https://unstats.un.org/SDGAPI/swagger/#/},
  urldate = {2023-02-20},
  howpublished = {\url{https://unstats.un.org/SDGAPI/swagger/#/}}
}

@article{wanghongshuai;lishantong;Kechixufazhanmubiaojianguanxileixingfenxi2021,
  title = {可持续发展目标间关系类型分析},
  author = {王红帅;李善同;},
  date = {2021},
  journaltitle = {中国人口·资源与环境},
  pages = {154--160},
  issn = {1002-2104},
  abstract = {联合国2030年可持续发展目标(Sustainable Development Goals,SDGs)为全球可持续发展提供了新的评价框架和远景目标,但目标间关系的复杂性决定了17个发展目标无法同时实现、同时执行,关注可持续发展目标间相互独立、相互依赖的综合性质已经成为当前的研究热点。文章基于目标间的复杂性质,在回顾既有文献的基础上,发现协同和权衡是分析目标间关系的主要概念,庞大的目标体系也存在多种潜在的结构类型;同时,目标的主观性取舍、数据的来源和质量、评估理论和方法的应用、跨学科和部门的合作以及国家间的异质性都会影响目标间关系的研究。文章认为,精确定义目标间关系类型最需要的还是认识论层面的指导。从可持续发展经济学的角度出发,研究提出了一个区分目标间关系类型的分析模型,用目标间存在的基本关系类型,即协同和权衡,拓展了可持续性科学中有关强可持续性和弱可持续性的分析框架。以整合性为目的,理论上可持续发展目标间关系应该包括四种类型:共存关系、替代关系、包含关系、互补关系。同时,研究呼吁将协同和权衡关系纳入可持续性科学的分析框架,强调应从强可持续性的视角来理解和检验目标间关系;建议对可持续发展目标进行结构上的降维处理,最好的目标间关系应该是结构类型中的平衡。},
  issue = {09 vo 31},
  langid = {chinese},
  keywords = {协同关系,可持续发展目标,可持续发展经济学,权衡关系,结构类型},
  file = {/home/scenerymc/Zotero/storage/E3ZFA8GT/王红帅\;李善同\; - 2021 - 可持续发展目标间关系类型分析.pdf}
}

@article{zhuTradeoffsSynergiesAirpollutionrelated2022,
  title = {Trade-Offs and Synergies among Air-Pollution-Related {{SDGs}} as Well as Interactions between Air-Pollution-Related {{SDGs}} and Other {{SDGs}}},
  author = {Zhu, Junwei and Zhai, Yingjia and Feng, Shilan and Tan, Ya and Wei, Wendong},
  date = {2022-01},
  journaltitle = {Journal of Cleaner Production},
  shortjournal = {Journal of Cleaner Production},
  volume = {331},
  pages = {129890},
  issn = {09596526},
  doi = {10.1016/j.jclepro.2021.129890},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0959652621040609},
  urldate = {2023-02-17},
  langid = {english},
  file = {/home/scenerymc/Zotero/storage/PXHV3D3U/Zhu 等 - 2022 - Trade-offs and synergies among air-pollution-relat.pdf}
}


% 完成度计算
@article{bidarbakhtnia2020measuring,
  title={Measuring sustainable development goals (SDGs): An inclusive approach},
  author={Bidarbakhtnia, Arman},
  journal={Global Policy},
  volume={11},
  number={1},
  pages={56--67},
  year={2020},
  publisher={Wiley Online Library}
}

@book{smith2014standard,
  title={Standard deviations: Flawed assumptions, tortured data, and other ways to lie with statistics},
  author={Smith, Gary},
  year={2014},
  publisher={Abrams}
}

@online{sbert,
  title = {SentenceTransformers Documentation},
  url = {https://www.sbert.net/},
  urldate = {2023-02-20},
  langid = {american},
  howpublished = {\url{https://www.sbert.net/}}
}

@inreference{Correlation2022,
  title = {Correlation},
  booktitle = {Wikipedia},
  date = {2022-11-22T20:16:12Z},
  url = {https://en.wikipedia.org/w/index.php?title=Correlation&oldid=1123244609},
  urldate = {2023-02-20},
  abstract = {In statistics, correlation  or dependence  is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related.   Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice.  For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation). Formally, random variables are dependent if they do not satisfy a mathematical property of probabilistic independence.  In informal parlance, correlation is synonymous with dependence. However, when used in a technical sense, correlation refers to any of several specific types of mathematical operations between the tested variables and their respective expected values. Essentially, correlation is the measure of how two or more variables are related to one another.  There are several correlation coefficients, often denoted                         ρ                 \{\textbackslash displaystyle \textbackslash rho \}    or                         r                 \{\textbackslash displaystyle r\}   , measuring the degree of correlation.  The most common of these is the Pearson correlation coefficient, which is sensitive only to a linear relationship between two variables (which may be present even when one variable is a nonlinear function of the other).  Other correlation coefficients – such as Spearman's rank correlation – have been developed to be more robust than Pearson's, that is, more sensitive to nonlinear relationships. Mutual information can also be applied to measure dependence between two variables.},
  langid = {english},
  annotation = {Page Version ID: 1123244609},
  howpublished = {\url{https://en.wikipedia.org/w/index.php?title=Correlation&oldid=1123244609}}
}

@article{reimersSentenceBERTSentenceEmbeddings2019,
  title = {Sentence-{{BERT}}: {{Sentence Embeddings}} Using {{Siamese BERT-Networks}}},
  shorttitle = {Sentence-{{BERT}}},
  author = {Reimers, Nils and Gurevych, Iryna},
  date = {2019},
  publisher = {{arXiv}},
  doi = {10.48550/ARXIV.1908.10084},
  url = {https://arxiv.org/abs/1908.10084},
  urldate = {2023-02-20},
  abstract = {BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (\textasciitilde 65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.},
  version = {1},
  keywords = {Computation and Language (cs.CL),FOS: Computer and information sciences}
}